def img_verification_task(self, split='DevTrain', dtype='uint8'):
lpaths, rpaths, labels = self.raw_verification_task(split)
limgs = larray.lmap(
utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype), lpaths)
rimgs = larray.lmap(
utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype), rpaths)
return limgs, rimgs, labels
def img_verification_task(self, split=None, dtype='uint8',
resplit=None, seed=0):
"""
use resplit to generate a resplitting of the view data
e.g. resplit='train_0' to get the training portion of the 0th split
seed initializes random number generator for resplitting
generation. default seed=0 generates standard "canonical" splits.
"""
assert resplit is None or split is None
if resplit is not None:
lpaths, rpaths, labels = self.raw_verification_task_resplit(resplit,
seed=seed)
else:
if split is None:
split = 'DevTrain'
lpaths, rpaths, labels = self.raw_verification_task(split)
limgs = larray.lmap(
utils.image.ImgLoader(shape=self.img_shape, dtype=dtype),
lpaths)
rimgs = larray.lmap(
utils.image.ImgLoader(shape=self.img_shape, dtype=dtype),
rpaths)
return limgs, rimgs, labels
def img_verification_task_from_raw(self, lpaths, rpaths, labels,
dtype='uint8'):
limgs = larray.lmap(
utils.image.ImgLoader(shape=self.img_shape, dtype=dtype),
lpaths)
rimgs = larray.lmap(
utils.image.ImgLoader(shape=self.img_shape, dtype=dtype),
rpaths)
return limgs, rimgs, labels
def img_verification_task(self, split='DevTrain', dtype='uint8'):
lpaths, rpaths, labels = self.raw_verification_task(split)
limgs = larray.lmap(
utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype),
lpaths)
rimgs = larray.lmap(
utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype),
rpaths)
return limgs, rimgs, labels
def main_show(cls):
# Usage one of:
# <driver> people
# <driver> pairs
from utils.glviewer import glumpy_viewer, command, glumpy
import larray
# print 'ARGV', sys.argv
try:
task = sys.argv[2]
except IndexError:
print >> sys.stderr, "Usage one of"
print >> sys.stderr, " <driver> lfw.<imgset> people"
print >> sys.stderr, " <driver> lfw.<imgset> pairs"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_train"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_test"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_10folds"
return 1
if task == "people":
self = cls()
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m["name"] for m in self.meta])
glumpy_viewer(img_array=larray.lmap(utils.image.load_rgb_f32, image_paths), arrays_to_print=[names])
elif task == "pairs" or sys.argv[2] == "pairs_train":
raise NotImplementedError()
elif task == "pairs_test":
raise NotImplementedError()
elif task == "pairs_10folds":
fold_num = int(sys.argv[3])
raise NotImplementedError()
if 0:
left_imgs = img_load(lpaths, slice_, color, resize)
right_imgs = img_load(rpaths, slice_, color, resize)
pairs = larray.lzip(left_imgs, right_imgs)
def main_show(cls):
# Usage one of:
# <driver> people
# <driver> pairs
from utils.glviewer import glumpy_viewer
try:
task = sys.argv[2]
except IndexError:
print >> sys.stderr, "Usage one of"
print >> sys.stderr, " <driver> lfw.<imgset> people"
print >> sys.stderr, " <driver> lfw.<imgset> pairs"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_train"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_test"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_10folds"
return 1
if task == 'people':
self = cls()
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m['name'] for m in self.meta])
glumpy_viewer(
img_array=larray.lmap(
utils.image.load_rgb_f32,
image_paths),
arrays_to_print=[names])
elif task == 'pairs' or sys.argv[2] == 'pairs_train':
raise NotImplementedError()
elif task == 'pairs_test':
raise NotImplementedError()
elif task == 'pairs_10folds':
raise NotImplementedError()
def img_classification_task(self, dtype='uint8', split=None):
img_paths, labels, inds = self.raw_classification_task(split=split)
imgs = larray.lmap(ImgLoader(shape=(100, 100, 3),
dtype=dtype,
mode='RGB'),
img_paths)
return imgs, labels
def main_show(cls):
# Usage one of:
# <driver> people
# <driver> pairs
from utils.glviewer import glumpy_viewer
try:
task = sys.argv[2]
except IndexError:
print >> sys.stderr, "Usage one of"
print >> sys.stderr, " <driver> lfw.<imgset> people"
print >> sys.stderr, " <driver> lfw.<imgset> pairs"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_train"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_test"
print >> sys.stderr, " <driver> lfw.<imgset> pairs_10folds"
return 1
if task == 'people':
self = cls()
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m['name'] for m in self.meta])
glumpy_viewer(img_array=larray.lmap(utils.image.load_rgb_f32,
image_paths),
arrays_to_print=[names])
elif task == 'pairs' or sys.argv[2] == 'pairs_train':
raise NotImplementedError()
elif task == 'pairs_test':
raise NotImplementedError()
elif task == 'pairs_10folds':
raise NotImplementedError()
def main_show():
"""compatibility with bin/datasets-show"""
from utils.glviewer import glumpy_viewer
import larray
pf = PubFig83()
names = [m['name'] for m in pf.meta]
paths = [pf.image_path(m) for m in pf.meta]
glumpy_viewer(img_array=larray.lmap(utils.image.ImgLoader(), paths),
arrays_to_print=[names])
def main_show():
"""compatibility with bin/datasets-show"""
from utils.glviewer import glumpy_viewer
import larray
pf = PubFig83()
names = [m['name'] for m in pf.meta]
paths = [pf.image_path(m) for m in pf.meta]
glumpy_viewer(
img_array=larray.lmap(utils.image.ImgLoader(), paths),
arrays_to_print=[names])
def img_recognition_task(self, dtype="uint8"):
img_paths, labels = self.raw_recognition_task()
imgs = larray.lmap(utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype), img_paths)
return imgs, labels
def img_classification_task(self, dtype="uint8", split=None):
img_paths, labels = self.raw_classification_task(split=split)
imgs = larray.lmap(ImgLoader(ndim=2, shape=(400, 400), dtype=dtype, mode="L"), img_paths)
return imgs, labels
def img_classification_task(self, dtype='uint8', split=None):
img_paths, labels = self.raw_classification_task(split=split)
imgs = larray.lmap(ImgLoader(ndim=3, dtype=dtype, mode='RGB'),
img_paths)
return imgs, labels
def img_classification_task(self, dtype='uint8'):
img_paths, labels = self.raw_classification_task()
imgs = larray.lmap(
utils.image.ImgLoader(shape=self.img_shape, dtype=dtype),
img_paths)
return imgs, labels
def img_recognition_task(self, dtype='uint8'):
img_paths, labels = self.raw_recognition_task()
imgs = larray.lmap(
utils.image.ImgLoader(shape=(250, 250, 3), dtype=dtype), img_paths)
return imgs, labels
